Systems and method for generating search terms

ABSTRACT

A method for generating keywords for searches, comprising the steps of retrieving search metric data comprising a plurality of search strings and interaction data; retrieving a plurality of first product identifiers each having one or more first attributes; generating, a table comprising the plurality of search strings ranked by interaction data; generating relevant lists comprising the plurality of search strings having interaction data above threshold values; retrieving data relating to a second product identifier; extracting one or more second attributes of the second product identifier; performing searches in the relevant lists using the second attribute data; assigning keywords to the data relating to the second identifier.

TECHNICAL FIELD

The present disclosure generally relates to computerized systems andmethods for generating machine searchable keywords. In particular,embodiments of the present disclosure relate to inventive andunconventional systems relate to generating machine searchable keywordsfor data entries stored in databases.

BACKGROUND

In the field of on-line retail business, information relating to avariety of products are stored in databases. When a shopper browsesdisplay interfaces of the on-line retail business, server systemsretrieve this information from the databases for display to the shopper.It is typical for the shopper to conduct searches for products byproviding to the server systems, search strings. The search strings mayinclude terms relating to brand name, generic name, model name, number,color, year, category, or other attributes that the shopper mayassociate with a product. The server systems may look for entries in thedatabases corresponding to products that match one or more of the termsin the search strings. When matches are found, the entries of thecorresponding matched products are return in a result list to bedisplayed to the shopper.

Thus, the quality of the results (i.e. relevancy of the results to theshopper's search) may largely depend on whether database entries ofproducts contain sufficient relevant keywords such that the shopper'ssearch string would likely result in correct matches. For example, aproduct having a database entry with few keywords are unlikely to befound in a shopper's search, even if it is highly relevant to thesearch.

Existing methods and systems rely on human individuals to provide suchkeywords in the database for the entries of the products. This isinefficient, and can be impractical if the number of database entries islarge. Moreover, updates to the entries to add or remove keywords may beprohibitively costly if human interventions are required for each entry.Therefore, there is a need for improved methods and systems to ensurethat keywords are generated and updated automatically without humanintervention.

SUMMARY

One aspect of the present disclosure is directed to a method forgenerating keywords for searches, comprising the steps of retrieving,from one or more database, search metric data for a predetermined timeperiod, the search metric comprise at least a plurality of searchstrings, and interaction data corresponding to each of the plurality ofsearch strings; retrieving, from the one or more database, a pluralityof first product identifiers associated with the interaction data, theplurality of first product identifiers each having one or more firstattributes; generating, based on the search metric data and theplurality of first product identifiers, a table, the table comprisingthe plurality of search strings ranked by the corresponding interactiondata; generating one or more relevant lists, the relevant lists comprisethe plurality of search strings having corresponding interaction dataabove one or more threshold values; retrieving, from the one or moredata base, data relating to a second product identifier; extracting,from the data, one or more second attributes of the second productidentifier; performing searches in the relevant lists using the one ormore second attribute data; assigning, based on a predetermined rule,keywords to the data relating to the second identifier, the keywordsbeing one or more of the plurality of search strings.

Another aspect of the present disclosure is directed to a computerizedsystem for generating keywords for searches, comprising: one or moreprocessors; storage media containing instructions to cause the one ormore processors to execute the steps of: retrieving, from one or moredatabase, search metric data for a predetermined time period, the searchmetric comprise at least a plurality of search strings, and interactiondata corresponding to each of the plurality of search strings;retrieving, from the one or more database, a plurality of first productidentifiers associated with the interaction data, the plurality of firstproduct identifiers each having one or more first attributes;generating, based on the search metric data and the plurality of firstproduct identifiers, a table, the table comprising the plurality ofsearch strings ranked by the corresponding interaction data; generatingone or more relevant lists, the relevant lists comprise the plurality ofsearch strings having corresponding interaction data above one or morethreshold values; retrieving, from the one or more data base, datarelating to a second product identifier; extracting, from the data, oneor more second attributes of the second product identifier; performingsearches in the relevant lists using the one or more second attributedata; assigning, based on a predetermined rule, keywords to the datarelating to the second identifier, the keywords being one or more of theplurality of search strings.

Yet another aspect of the present disclosure is directed to a system forgenerating keywords for searches, comprising; retrieving, from one ormore database, search metric data for a predetermined time period, thesearch metric comprise at least a plurality of search strings, andinteraction data corresponding to each of the plurality of searchstrings; retrieving, from the one or more database, a plurality of firstproduct identifiers associated with the interaction data, the pluralityof first product identifiers each having one or more first attributes;generating, based on the search metric data and the plurality of firstproduct identifiers, a table, the table comprising the plurality ofsearch strings ranked by the corresponding interaction data; generatingone or more relevant lists, the relevant lists comprise the plurality ofsearch strings having corresponding interaction data above one or morethreshold values; receiving, from one or more user device, productinformation of a second product identifier comprising at least a productname; extracting, based on the product name, one or more secondattributes data of the second product identifier; performing searches inthe relevant lists using the one or more attribute data; assigning,based on a predetermined rule, keywords to the data relating to thesecond identifier, the keywords being one or more of the plurality ofsearch strings.

Other systems, methods, and computer-readable media are also discussedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic block diagram illustrating an exemplaryembodiment of a network comprising computerized systems forcommunications enabling shipping, transportation, and logisticsoperations, consistent with the disclosed embodiments.

FIG. 1B depicts a sample Search Result Page (SRP) that includes one ormore search results satisfying a search request along with interactiveuser interface elements, consistent with the disclosed embodiments.

FIG. 1C depicts a sample Single Detail Page (SDP) that includes aproduct and information about the product along with interactive userinterface elements, consistent with the disclosed embodiments.

FIG. 1D depicts a sample Cart page that includes items in a virtualshopping cart along with interactive user interface elements, consistentwith the disclosed embodiments.

FIG. 1E depicts a sample Order page that includes items from the virtualshopping cart along with information regarding purchase and shipping,along with interactive user interface elements, consistent with thedisclosed embodiments.

FIG. 2 is a diagrammatic illustration of an exemplary fulfillment centerconfigured to utilize disclosed computerized systems, consistent withthe disclosed embodiments.

FIG. 3 is a diagrammatic illustration of an exemplary system forgenerating keywords for searches, consistent with the disclosedembodiments.

FIG. 4 is a diagrammatic illustration of examples of data or informationassociated with a first product identifier in table, consistent with thedisclosed embodiments.

FIG. 5 is a flow chart depicting an exemplary process for generatingkeywords for searches, consistent with the disclosed embodiments.

FIG. 6 is a flow chart depicting an exemplary process for generatingkeywords for searches consistent with the disclosed embodiments.

FIG. 7 depicts an illustration exemplary process for determining aprobability of matching, consistent with the disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions, or modifications may be made to thecomponents and steps illustrated in the drawings, and the illustrativemethods described herein may be modified by substituting, reordering,removing, or adding steps to the disclosed methods. Accordingly, thefollowing detailed description is not limited to the disclosedembodiments and examples. Instead, the proper scope of the invention isdefined by the appended claims.

According various embodiments of the present disclosure, there areprovided unconventional computer implemented systems for generatingsearchable strings for entries in one or more databases. Whereas priorsystems may obtain searchable strings from individuals or systems thatgenerates these searchable strings or maintains the databases, thevarious embodiments of the present disclosure may rely on search queriessupplied to the database systems to generate searchable strings forentries in the databases, thus greatly improving the quality andrelevancy of search results presented to the searchers.

Referring to FIG. 1A, a schematic block diagram 100 illustrating anexemplary embodiment of a system comprising computerized systems forcommunications enabling shipping, transportation, and logisticsoperations is shown. As illustrated in FIG. 1A, system 100 may include avariety of systems, each of which may be connected to one another viaone or more networks. The systems may also be connected to one anothervia a direct connection, for example, using a cable. The depictedsystems include a shipment authority technology (SAT) system 101, anexternal front end system 103, an internal front end system 105, atransportation system 107, mobile devices 107A, 107B, and 107C, sellerportal 109, shipment and order tracking (SOT) system 111, fulfillmentoptimization (FO) system 113, fulfillment messaging gateway (FMG) 115,supply chain management (SCM) system 117, warehouse management system119, mobile devices 119A, 119B, and 119C (depicted as being inside offulfillment center (FC) 200), 3^(rd) party fulfillment systems 121A,121B, and 121C, fulfillment center authorization system (FC Auth) 123,and labor management system (LMS) 125.

SAT system 101, in some embodiments, may be implemented as a computersystem that monitors order status and delivery status. For example, SATsystem 101 may determine whether an order is past its Promised DeliveryDate (PDD) and may take appropriate action, including initiating a neworder, reshipping the items in the non-delivered order, canceling thenon-delivered order, initiating contact with the ordering customer, orthe like. SAT system 101 may also monitor other data, including output(such as a number of packages shipped during a particular time period)and input (such as the number of empty cardboard boxes received for usein shipping). SAT system 101 may also act as a gateway between differentdevices in system 100, enabling communication (e.g., usingstore-and-forward or other techniques) between devices such as externalfront end system 103 and FO system 113.

External front end system 103, in some embodiments, may be implementedas a computer system that enables external users to interact with one ormore systems in system 100. For example, in embodiments where system 100enables the presentation of systems to enable users to place an orderfor an item, external front end system 103 may be implemented as a webserver that receives search requests, presents item pages, and solicitspayment information. For example, external front end system 103 may beimplemented as a computer or computers running software such as theApache HTTP Server, Microsoft Internet Information Services (IIS),NGINX, or the like. In other embodiments, external front end system 103may run custom web server software designed to receive and processrequests from external devices (e.g., mobile device 102A or computer102B), acquire information from databases and other data stores based onthose requests, and provide responses to the received requests based onacquired information.

In some embodiments, external front end system 103 may include one ormore of a web caching system, a database, a search system, or a paymentsystem. In one aspect, external front end system 103 may comprise one ormore of these systems, while in another aspect, external front endsystem 103 may comprise interfaces (e.g., server-to-server,database-to-database, or other network connections) connected to one ormore of these systems.

An illustrative set of steps, illustrated by FIGS. 1B, 1C, 1D, and 1E,will help to describe some operations of external front end system 103.External front end system 103 may receive information from systems ordevices in system 100 for presentation and/or display. For example,external front end system 103 may host or provide one or more web pages,including a Search Result Page (SRP) (e.g., FIG. 1B), a Single DetailPage (SDP) (e.g., FIG. 1C), a Cart page (e.g., FIG. 1D), or an Orderpage (e.g., FIG. 1E). A user device (e.g., using mobile device 102A orcomputer 102B) may navigate to external front end system 103 and requesta search by entering information into a search box. External front endsystem 103 may request information from one or more systems in system100. For example, external front end system 103 may request informationfrom FO System 113 that satisfies the search request. External front endsystem 103 may also request and receive (from FO System 113) a PromisedDelivery Date or “PDD” for each product included in the search results.The PDD, in some embodiments, may represent an estimate of when apackage containing the product will arrive at the user's desiredlocation or a date by which the product is promised to be delivered atthe user's desired location if ordered within a particular period oftime, for example, by the end of the day (11:59 PM). (PDD is discussedfurther below with respect to FO System 113.)

External front end system 103 may prepare an SRP (e.g., FIG. 1B) basedon the information. The SRP may include information that satisfies thesearch request. For example, this may include pictures of products thatsatisfy the search request. The SRP may also include respective pricesfor each product, or information relating to enhanced delivery optionsfor each product, PDD, weight, size, offers, discounts, or the like.External front end system 103 may send the SRP to the requesting userdevice (e.g., via a network).

A user device may then select a product from the SRP, e.g., by clickingor tapping a user interface, or using another input device, to select aproduct represented on the SRP. The user device may formulate a requestfor information on the selected product and send it to external frontend system 103. In response, external front end system 103 may requestinformation related to the selected product. For example, theinformation may include additional information beyond that presented fora product on the respective SRP. This could include, for example, shelflife, country of origin, weight, size, number of items in package,handling instructions, or other information about the product. Theinformation could also include recommendations for similar products(based on, for example, big data and/or machine learning analysis ofcustomers who bought this product and at least one other product),answers to frequently asked questions, reviews from customers,manufacturer information, pictures, or the like.

External front end system 103 may prepare an SDP (Single Detail Page)(e.g., FIG. 1C) based on the received product information. The SDP mayalso include other interactive elements such as a “Buy Now” button, a“Add to Cart” button, a quantity field, a picture of the item, or thelike. The SDP may further include a list of sellers that offer theproduct. The list may be ordered based on the price each seller offerssuch that the seller that offers to sell the product at the lowest pricemay be listed at the top. The list may also be ordered based on theseller ranking such that the highest ranked seller may be listed at thetop. The seller ranking may be formulated based on multiple factors,including, for example, the seller's past track record of meeting apromised PDD. External front end system 103 may deliver the SDP to therequesting user device (e.g., via a network).

The requesting user device may receive the SDP which lists the productinformation. Upon receiving the SDP, the user device may then interactwith the SDP. For example, a user of the requesting user device mayclick or otherwise interact with a “Place in Cart” button on the SDP.This adds the product to a shopping cart associated with the user. Theuser device may transmit this request to add the product to the shoppingcart to external front end system 103.

External front end system 103 may generate a Cart page (e.g., FIG. 1D).The Cart page, in some embodiments, lists the products that the user hasadded to a virtual “shopping cart.” A user device may request the Cartpage by clicking on or otherwise interacting with an icon on the SRP,SDP, or other pages. The Cart page may, in some embodiments, list allproducts that the user has added to the shopping cart, as well asinformation about the products in the cart such as a quantity of eachproduct, a price for each product per item, a price for each productbased on an associated quantity, information regarding PDD, a deliverymethod, a shipping cost, user interface elements for modifying theproducts in the shopping cart (e.g., deletion or modification of aquantity), options for ordering other product or setting up periodicdelivery of products, options for setting up interest payments, userinterface elements for proceeding to purchase, or the like. A user at auser device may click on or otherwise interact with a user interfaceelement (e.g., a button that reads “Buy Now”) to initiate the purchaseof the product in the shopping cart. Upon doing so, the user device maytransmit this request to initiate the purchase to external front endsystem 103.

External front end system 103 may generate an Order page (e.g., FIG. 1E)in response to receiving the request to initiate a purchase. The Orderpage, in some embodiments, re-lists the items from the shopping cart andrequests input of payment and shipping information. For example, theOrder page may include a section requesting information about thepurchaser of the items in the shopping cart (e.g., name, address, e-mailaddress, phone number), information about the recipient (e.g., name,address, phone number, delivery information), shipping information(e.g., speed/method of delivery and/or pickup), payment information(e.g., credit card, bank transfer, check, stored credit), user interfaceelements to request a cash receipt (e.g., for tax purposes), or thelike. External front end system 103 may send the Order page to the userdevice.

The user device may enter information on the Order page and click orotherwise interact with a user interface element that sends theinformation to external front end system 103. From there, external frontend system 103 may send the information to different systems in system100 to enable the creation and processing of a new order with theproducts in the shopping cart.

In some embodiments, external front end system 103 may be furtherconfigured to enable sellers to transmit and receive informationrelating to orders.

Internal front end system 105, in some embodiments, may be implementedas a computer system that enables internal users (e.g., employees of anorganization that owns, operates, or leases system 100) to interact withone or more systems in system 100. For example, in embodiments wheresystem 100 enables the presentation of systems to enable users to placean order for an item, internal front end system 105 may be implementedas a web server that enables internal users to view diagnostic andstatistical information about orders, modify item information, or reviewstatistics relating to orders. For example, internal front end system105 may be implemented as a computer or computers running software suchas the Apache HTTP Server, Microsoft Internet Information Services(IIS), NGINX, or the like. In other embodiments, internal front endsystem 105 may run custom web server software designed to receive andprocess requests from systems or devices depicted in system 100 (as wellas other devices not depicted), acquire information from databases andother data stores based on those requests, and provide responses to thereceived requests based on acquired information.

In some embodiments, internal front end system 105 may include one ormore of a web caching system, a database, a search system, a paymentsystem, an analytics system, an order monitoring system, or the like. Inone aspect, internal front end system 105 may comprise one or more ofthese systems, while in another aspect, internal front end system 105may comprise interfaces (e.g., server-to-server, database-to-database,or other network connections) connected to one or more of these systems.

Transportation system 107, in some embodiments, may be implemented as acomputer system that enables communication between systems or devices insystem 100 and mobile devices 107A-107C. Transportation system 107, insome embodiments, may receive information from one or more mobiledevices 107A-107C (e.g., mobile phones, smart phones, PDAs, or thelike). For example, in some embodiments, mobile devices 107A-107C maycomprise devices operated by delivery workers. The delivery workers, whomay be permanent, temporary, or shift employees, may utilize mobiledevices 107A-107C to effect delivery of packages containing the productsordered by users. For example, to deliver a package, the delivery workermay receive a notification on a mobile device indicating which packageto deliver and where to deliver it. Upon arriving at the deliverylocation, the delivery worker may locate the package (e.g., in the backof a truck or in a crate of packages), scan or otherwise capture dataassociated with an identifier on the package (e.g., a barcode, an image,a text string, an RFID tag, or the like) using the mobile device, anddeliver the package (e.g., by leaving it at a front door, leaving itwith a security guard, handing it to the recipient, or the like). Insome embodiments, the delivery worker may capture photo(s) of thepackage and/or may obtain a signature using the mobile device. Themobile device may send information to transportation system 107including information about the delivery, including, for example, time,date, GPS location, photo(s), an identifier associated with the deliveryworker, an identifier associated with the mobile device, or the like.Transportation system 107 may store this information in a database (notpictured) for access by other systems in system 100. Transportationsystem 107 may, in some embodiments, use this information to prepare andsend tracking data to other systems indicating the location of aparticular package.

In some embodiments, certain users may use one kind of mobile device(e.g., permanent workers may use a specialized PDA with custom hardwaresuch as a barcode scanner, stylus, and other devices) while other usersmay use other kinds of mobile devices (e.g., temporary or shift workersmay utilize off-the-shelf mobile phones and/or smartphones).

In some embodiments, transportation system 107 may associate a user witheach device. For example, transportation system 107 may store anassociation between a user (represented by, e.g., a user identifier, anemployee identifier, or a phone number) and a mobile device (representedby, e.g., an International Mobile Equipment Identity (IMEI), anInternational Mobile Subscription Identifier (IMSI), a phone number, aUniversal Unique Identifier (UUID), or a Globally Unique Identifier(GUID)). Transportation system 107 may use this association inconjunction with data received on deliveries to analyze data stored inthe database in order to determine, among other things, a location ofthe worker, an efficiency of the worker, or a speed of the worker.

Seller portal 109, in some embodiments, may be implemented as a computersystem that enables sellers or other external entities to electronicallycommunicate with one or more systems in system 100. For example, aseller may utilize a computer system (not pictured) to upload or provideproduct information, order information, contact information, or thelike, for products that the seller wishes to sell through system 100using seller portal 109.

Shipment and order tracking system 111, in some embodiments, may beimplemented as a computer system that receives, stores, and forwardsinformation regarding the location of packages containing productsordered by customers (e.g., by a user using devices 102A-102B). In someembodiments, shipment and order tracking system 111 may request or storeinformation from web servers (not pictured) operated by shippingcompanies that deliver packages containing products ordered bycustomers.

In some embodiments, shipment and order tracking system 111 may requestand store information from systems depicted in system 100. For example,shipment and order tracking system 111 may request information fromtransportation system 107. As discussed above, transportation system 107may receive information from one or more mobile devices 107A-107C (e.g.,mobile phones, smart phones, PDAs, or the like) that are associated withone or more of a user (e.g., a delivery worker) or a vehicle (e.g., adelivery truck). In some embodiments, shipment and order tracking system111 may also request information from warehouse management system (WMS)119 to determine the location of individual products inside of afulfillment center (e.g., fulfillment center 200). Shipment and ordertracking system 111 may request data from one or more of transportationsystem 107 or WMS 119, process it, and present it to a device (e.g.,user devices 102A and 102B) upon request.

Fulfillment optimization (FO) system 113, in some embodiments, may beimplemented as a computer system that stores information for customerorders from other systems (e.g., external front end system 103 and/orshipment and order tracking system 111). FO system 113 may also storeinformation describing where particular items are held or stored. Forexample, certain items may be stored only in one fulfillment center,while certain other items may be stored in multiple fulfillment centers.In still other embodiments, certain fulfilment centers may be designedto store only a particular set of items (e.g., fresh produce or frozenproducts). FO system 113 stores this information as well as associatedinformation (e.g., quantity, size, date of receipt, expiration date,etc.).

FO system 113 may also calculate a corresponding PDD (promised deliverydate) for each product. The PDD, in some embodiments, may be based onone or more factors. For example, FO system 113 may calculate a PDD fora product based on a past demand for a product (e.g., how many timesthat product was ordered during a period of time), an expected demandfor a product (e.g., how many customers are forecast to order theproduct during an upcoming period of time), a network-wide past demandindicating how many products were ordered during a period of time, anetwork-wide expected demand indicating how many products are expectedto be ordered during an upcoming period of time, one or more counts ofthe product stored in each fulfillment center 200, which fulfillmentcenter stores each product, expected or current orders for that product,or the like.

In some embodiments, FO system 113 may determine a PDD for each producton a periodic basis (e.g., hourly) and store it in a database forretrieval or sending to other systems (e.g., external front end system103, SAT system 101, shipment and order tracking system 111). In otherembodiments, FO system 113 may receive electronic requests from one ormore systems (e.g., external front end system 103, SAT system 101,shipment and order tracking system 111) and calculate the PDD on demand.

Fulfilment messaging gateway (FMG) 115, in some embodiments, may beimplemented as a computer system that receives a request or response inone format or protocol from one or more systems in system 100, such asFO system 113, converts it to another format or protocol, and forward itin the converted format or protocol to other systems, such as WMS 119 or3^(rd) party fulfillment systems 121A, 121B, or 121C, and vice versa.

Supply chain management (SCM) system 117, in some embodiments, may beimplemented as a computer system that performs forecasting functions.For example, SCM system 117 may forecast a level of demand for aparticular product based on, for example, based on a past demand forproducts, an expected demand for a product, a network-wide past demand,a network-wide expected demand, a count products stored in eachfulfillment center 200, expected or current orders for each product, orthe like. In response to this forecasted level and the amount of eachproduct across all fulfillment centers, SCM system 117 may generate oneor more purchase orders to purchase and stock a sufficient quantity tosatisfy the forecasted demand for a particular product.

Warehouse management system (WMS) 119, in some embodiments, may beimplemented as a computer system that monitors workflow. For example,WMS 119 may receive event data from individual devices (e.g., devices107A-107C or 119A-119C) indicating discrete events. For example, WMS 119may receive event data indicating the use of one of these devices toscan a package. As discussed below with respect to fulfillment center200 and FIG. 2, during the fulfillment process, a package identifier(e.g., a barcode or RFID tag data) may be scanned or read by machines atparticular stages (e.g., automated or handheld barcode scanners, RFIDreaders, high-speed cameras, devices such as tablet 119A, mobiledevice/PDA 119B, computer 119C, or the like). WMS 119 may store eachevent indicating a scan or a read of a package identifier in acorresponding database (not pictured) along with the package identifier,a time, date, location, user identifier, or other information, and mayprovide this information to other systems (e.g., shipment and ordertracking system 111).

WMS 119, in some embodiments, may store information associating one ormore devices (e.g., devices 107A-107C or 119A-119C) with one or moreusers associated with system 100. For example, in some situations, auser (such as a part- or full-time employee) may be associated with amobile device in that the user owns the mobile device (e.g., the mobiledevice is a smartphone). In other situations, a user may be associatedwith a mobile device in that the user is temporarily in custody of themobile device (e.g., the user checked the mobile device out at the startof the day, will use it during the day, and will return it at the end ofthe day).

WMS 119, in some embodiments, may maintain a work log for each userassociated with system 100. For example, WMS 119 may store informationassociated with each employee, including any assigned processes (e.g.,unloading trucks, picking items from a pick zone, rebin wall work,packing items), a user identifier, a location (e.g., a floor or zone ina fulfillment center 200), a number of units moved through the system bythe employee (e.g., number of items picked, number of items packed), anidentifier associated with a device (e.g., devices 119A-119C), or thelike. In some embodiments, WMS 119 may receive check-in and check-outinformation from a timekeeping system, such as a timekeeping systemoperated on a device 119A-119C.

3^(rd) party fulfillment (3PL) systems 121A-121C, in some embodiments,represent computer systems associated with third-party providers oflogistics and products. For example, while some products are stored infulfillment center 200 (as discussed below with respect to FIG. 2),other products may be stored off-site, may be produced on demand, or maybe otherwise unavailable for storage in fulfillment center 200. 3PLsystems 121A-121C may be configured to receive orders from FO system 113(e.g., through FMG 115) and may provide products and/or services (e.g.,delivery or installation) to customers directly. In some embodiments,one or more of 3PL systems 121A-121C may be part of system 100, while inother embodiments, one or more of 3PL systems 121A-121C may be outsideof system 100 (e.g., owned or operated by a third-party provider).

Fulfillment Center Auth system (FC Auth) 123, in some embodiments, maybe implemented as a computer system with a variety of functions. Forexample, in some embodiments, FC Auth 123 may act as a single-sign on(SSO) service for one or more other systems in system 100. For example,FC Auth 123 may enable a user to log in via internal front end system105, determine that the user has similar privileges to access resourcesat shipment and order tracking system 111, and enable the user to accessthose privileges without requiring a second log in process. FC Auth 123,in other embodiments, may enable users (e.g., employees) to associatethemselves with a particular task. For example, some employees may nothave an electronic device (such as devices 119A-119C) and may insteadmove from task to task, and zone to zone, within a fulfillment center200, during the course of a day. FC Auth 123 may be configured to enablethose employees to indicate what task they are performing and what zonethey are in at different times of day.

Labor management system (LMS) 125, in some embodiments, may beimplemented as a computer system that stores attendance and overtimeinformation for employees (including full-time and part-time employees).For example, LMS 125 may receive information from FC Auth 123, WMS 119,devices 119A-119C, transportation system 107, and/or devices 107A-107C.

The particular configuration depicted in FIG. 1A is an example only. Forexample, while FIG. 1A depicts FC Auth system 123 connected to FO system113, not all embodiments require this particular configuration. Indeed,in some embodiments, the systems in system 100 may be connected to oneanother through one or more public or private networks, including theInternet, an Intranet, a WAN (Wide-Area Network), a MAN(Metropolitan-Area Network), a wireless network compliant with the IEEE802.11a/b/g/n Standards, a leased line, or the like. In someembodiments, one or more of the systems in system 100 may be implementedas one or more virtual servers implemented at a data center, serverfarm, or the like.

FIG. 2 depicts a fulfillment center 200. Fulfillment center 200 is anexample of a physical location that stores items for shipping tocustomers when ordered. Fulfillment center (FC) 200 may be divided intomultiple zones, each of which are depicted in FIG. 2. These “zones,” insome embodiments, may be thought of as virtual divisions betweendifferent stages of a process of receiving items, storing the items,retrieving the items, and shipping the items. So while the “zones” aredepicted in FIG. 2, other divisions of zones are possible, and the zonesin FIG. 2 may be omitted, duplicated, or modified in some embodiments.

Inbound zone 203 represents an area of FC 200 where items are receivedfrom sellers who wish to sell products using system 100 from FIG. 1A.For example, a seller may deliver items 202A and 202B using truck 201.Item 202A may represent a single item large enough to occupy its ownshipping pallet, while item 202B may represent a set of items that arestacked together on the same pallet to save space.

A worker will receive the items in inbound zone 203 and may optionallycheck the items for damage and correctness using a computer system (notpictured). For example, the worker may use a computer system to comparethe quantity of items 202A and 202B to an ordered quantity of items. Ifthe quantity does not match, that worker may refuse one or more of items202A or 202B. If the quantity does match, the worker may move thoseitems (using, e.g., a dolly, a handtruck, a forklift, or manually) tobuffer zone 205. Buffer zone 205 may be a temporary storage area foritems that are not currently needed in the picking zone, for example,because there is a high enough quantity of that item in the picking zoneto satisfy forecasted demand. In some embodiments, forklifts 206 operateto move items around buffer zone 205 and between inbound zone 203 anddrop zone 207. If there is a need for items 202A or 202B in the pickingzone (e.g., because of forecasted demand), a forklift may move items202A or 202B to drop zone 207.

Drop zone 207 may be an area of FC 200 that stores items before they aremoved to picking zone 209. A worker assigned to the picking task (a“picker”) may approach items 202A and 202B in the picking zone, scan abarcode for the picking zone, and scan barcodes associated with items202A and 202B using a mobile device (e.g., device 119B). The picker maythen take the item to picking zone 209 (e.g., by placing it on a cart orcarrying it).

Picking zone 209 may be an area of FC 200 where items 208 are stored onstorage units 210. In some embodiments, storage units 210 may compriseone or more of physical shelving, bookshelves, boxes, totes,refrigerators, freezers, cold stores, or the like. In some embodiments,picking zone 209 may be organized into multiple floors. In someembodiments, workers or machines may move items into picking zone 209 inmultiple ways, including, for example, a forklift, an elevator, aconveyor belt, a cart, a handtruck, a dolly, an automated robot ordevice, or manually. For example, a picker may place items 202A and 202Bon a handtruck or cart in drop zone 207 and walk items 202A and 202B topicking zone 209.

A picker may receive an instruction to place (or “stow”) the items inparticular spots in picking zone 209, such as a particular space on astorage unit 210. For example, a picker may scan item 202A using amobile device (e.g., device 119B). The device may indicate where thepicker should stow item 202A, for example, using a system that indicatean aisle, shelf, and location. The device may then prompt the picker toscan a barcode at that location before stowing item 202A in thatlocation. The device may send (e.g., via a wireless network) data to acomputer system such as WMS 119 in FIG. 1A indicating that item 202A hasbeen stowed at the location by the user using device 1198.

Once a user places an order, a picker may receive an instruction ondevice 1198 to retrieve one or more items 208 from storage unit 210. Thepicker may retrieve item 208, scan a barcode on item 208, and place iton transport mechanism 214. While transport mechanism 214 is representedas a slide, in some embodiments, transport mechanism may be implementedas one or more of a conveyor belt, an elevator, a cart, a forklift, ahandtruck, a dolly, a cart, or the like. Item 208 may then arrive atpacking zone 211.

Packing zone 211 may be an area of FC 200 where items are received frompicking zone 209 and packed into boxes or bags for eventual shipping tocustomers. In packing zone 211, a worker assigned to receiving items (a“rebin worker”) will receive item 208 from picking zone 209 anddetermine what order it corresponds to. For example, the rebin workermay use a device, such as computer 119C, to scan a barcode on item 208.Computer 119C may indicate visually which order item 208 is associatedwith. This may include, for example, a space or “cell” on a wall 216that corresponds to an order. Once the order is complete (e.g., becausethe cell contains all items for the order), the rebin worker mayindicate to a packing worker (or “packer”) that the order is complete.The packer may retrieve the items from the cell and place them in a boxor bag for shipping. The packer may then send the box or bag to a hubzone 213, e.g., via forklift, cart, dolly, handtruck, conveyor belt,manually, or otherwise.

Hub zone 213 may be an area of FC 200 that receives all boxes or bags(“packages”) from packing zone 211. Workers and/or machines in hub zone213 may retrieve package 218 and determine which portion of a deliveryarea each package is intended to go to, and route the package to anappropriate camp zone 215. For example, if the delivery area has twosmaller sub-areas, packages will go to one of two camp zones 215. Insome embodiments, a worker or machine may scan a package (e.g., usingone of devices 119A-119C) to determine its eventual destination. Routingthe package to camp zone 215 may comprise, for example, determining aportion of a geographical area that the package is destined for (e.g.,based on a postal code) and determining a camp zone 215 associated withthe portion of the geographical area.

Camp zone 215, in some embodiments, may comprise one or more buildings,one or more physical spaces, or one or more areas, where packages arereceived from hub zone 213 for sorting into routes and/or sub-routes. Insome embodiments, camp zone 215 is physically separate from FC 200 whilein other embodiments camp zone 215 may form a part of FC 200.

Workers and/or machines in camp zone 215 may determine which routeand/or sub-route a package 220 should be associated with, for example,based on a comparison of the destination to an existing route and/orsub-route, a calculation of workload for each route and/or sub-route,the time of day, a shipping method, the cost to ship the package 220, aPDD associated with the items in package 220, or the like. In someembodiments, a worker or machine may scan a package (e.g., using one ofdevices 119A-119C) to determine its eventual destination. Once package220 is assigned to a particular route and/or sub-route, a worker and/ormachine may move package 220 to be shipped. In exemplary FIG. 2, campzone 215 includes a truck 222, a car 226, and delivery workers 224A and224B. In some embodiments, truck 222 may be driven by delivery worker224A, where delivery worker 224A is a full-time employee that deliverspackages for FC 200 and truck 222 is owned, leased, or operated by thesame company that owns, leases, or operates FC 200. In some embodiments,car 226 may be driven by delivery worker 224B, where delivery worker224B is a “flex” or occasional worker that is delivering on an as-neededbasis (e.g., seasonally). Car 226 may be owned, leased, or operated bydelivery worker 224B.

From time to time, on-line retail platform systems (“the systems”) maybe updated with new product for sale. Shoppers may be unfamiliar withnew products, and new products by their nature lack purchasing recordand/or endorsements, and may thus be disadvantaged when competingagainst existing products in listings or search results. The systems maycompensate for this by recommending new products ahead of existingproducts in listing and searches, or by presenting recommended newproducts in separate display regions. System 100 may be an example ofthe on-line retail platform systems.

However, such a recommendation process may rely on the systems beingable to contextualize the new product through its properties, and matchthat to potential demand. This may be accomplished by keywords, tags,attributes, and other similar text searchable strings associated withthe new product, such that the systems may compare the new product tothe existing catalog of products, and recommend the new product whenrelated an existing product is deemed to be relevant to a shopper'sinquiry. However, conventional systems and methods largely leave thegenerations of these keywords, tags, attributes, and other similar textsearchable strings to the sellers. This may be unreliable as manysellers may not be proficient at generating relevant terms for the newproduct. Moreover, this may be excessively burdensome if sellersregister a large quantity of new products in the system.

According to some embodiments, there are provided methods for generatingsearchable keywords. Keywords, in context of computer technology, mayrefer to a series of data bits that represent characters such asletters, numbers, punctuations, and/or other similar information. Insome embodiments, searchable keywords may be in the form of textstrings. A keyword may be “searchable” if is capable to be used togetherwith various different search functions. Searches, search operation orfunctions may refer to functions or steps performed by one or morelogics, programs, and/or algorithms, executed by one or computersystems, for locating information or data. An example of a searchoperation may include: receiving one or more character strings (i.e.,queries) representing target information or data that needs to belocated; executing one or more logics, programs, or algorithms tolocated the target information or data; and if the target information ordata is located, sending the located information or data back to thesearcher. According to some embodiments, there are provided systems forgenerating searchable keywords, the systems include one or moreprocessors and one or more memory storage media.

By way of example, FIG. 3 depicts diagrammatic illustration of anexemplary system for generating keywords for searches, consistent withthe disclosed embodiments. System 300 may include devices 302. Devices302 represent devices associates with users who searches through systemsand databases over a period of time. For example, users may use devices302 to search for products using search strings in system 100 and anyassociated databases. These searches generate search metric data, suchas the search strings used, the results generated, and the interactionbetween the users and the results. The search metric data may be storedin search DB 304. Users associated with devices 302 will hereafter bereferred to as “searcher” to distinguish from users associated with userdevice 312 or vendor device 314.

Server 306 may be a computing device including one or more processors,I/O sections, and memory storage media. Server 306 may retrieve, asinputs, data from entries in a first database, such as search database(Search DB) 304 and/or common data storage database (CDS DB) 308, andmay provide as output, processed data for storage in a second database,such as promotion DB 310. In some embodiments, data retrieved fromsearch DB 304 may be search metric data including searching strings,interaction data, and first product identifiers associated with thesearch strings. In some embodiments, server 306 receives attributescorresponding to the first product identifiers from CDS DB 308. Server306 may generate tables using the interaction data and the attributes.Using the generated tables, server 306 may generate keywords and assignthe keywords to second product identifier received from vendor device314. In some embodiments, server 306 may provide to promotion DB 310 thesecond product identifier having the assigned keywords.

Vendor device 314 may be a computing device that uploads data to asystem, such as server 306. In some embodiments, user devices 102A-C maybe examples of vendor device 314, and external front end system 103 mayinteract with user devices 102A-C to configure the uploaded data forprocessing. In some embodiments, vendor device 314 may be associatedwith a vendor who is the source of the product (e.g., a manufacturer orreseller) for sale on system 100. For example, the vendor who provide aproduct for sale on system 100 may provide (e.g., upload) data of theproduct, including, e.g., product name, color, brand, category, otherattributes (e.g. size, dimension, color, battery life), images, and/orother features and options that inform potential buyers of the product'snature and use.

User device 312 may be a computing device associated with users who maybe interacting with system 100 as shoppers. User devices 102A-C may beexamples of user device 312. In some embodiments, shoppers using userdevice 312 may perform searches for products. In some embodiments, userdevice 312 may interact with front end system 103 to perform searches insystem 100. Along with the results of the search, external front endsystem 103 may retrieve results from promotion DB 310 for display onuser device 312.

FIG. 4 is a diagrammatic illustration of examples of data or informationassociated with first product identifiers in table, consistent with thedisclosed embodiments. The table may be generated using process 500depicted in FIG. 5. Table 401 depicts an interaction table generated byserver 306. Column 402 includes search strings from devices 302. In thesimplified example of FIG. 4, devices 302 may have received searchstrings “Nike shoes white”, “Samsung TV 50″,” and “Sport shoes.” Column404 represents first product identifiers that were selected by searchersfor each of search strings in column 402, and column 406 represents afrequency of selection (e.g. clicks, selections) for the correspondingfirst product identifier in column 404. For example, searchers selectedproduct 11 for “Nike shoes white” 865 times; selected product 435 for“Nike shoes white” 34 times; selected product 11 for “Sport shoes” 76times; and selected product 34 for “Samsung TV 50″” 652 times.

Table 403 depicts a relevant list generated by server 306 based on table401, consistent with the disclosed embodiments. The relevant list mayinclude first product identifiers from table 401, and for each of thesefirst product identifiers. As depicted in FIG. 4, column 408 representthe first product identifiers, column 410 represents the search stringsassociated with the first product identifiers of column 408, and column412 represents the frequency of each of the corresponding searching incolumn 410. For example, based on table 401, column 408 contain product11 and product 34. For product 34, recall in table 401, searchersselected it for “Nike shoes white” 865 times, and for “Sport shoes” 76times, and thus column 410 and 412 includes this information. Theprocess of generating table 403 from table 401 will be described belowwith respect to FIG. 5.

FIG. 5 is a flow chart 500 depicting an exemplary process for generatingkeywords for searches, consistent with the disclosed embodiments.

According to some embodiments, the systems may retrieve, from one ormore databases, search metric data for a predetermined time period.Search metric data may refer to data that is generated or gathered fromsearch operations. Examples of search metrics may include, but notlimited to, number of search queries received, the character strings ofeach queries, number of searches performed, system errors, thetime/speed of the searches, the results of the searches, interactionswith the search results, and/or other data cataloging the operations ofthe searching algorithms and systems. Once generated, the search metricdata may be stored in databases (e.g., search DB 304). For example,searchers may use devices 302 to conduct searches for products in system100. System 100, or one of its sub-system (e.g. external front system103), may generate search metric data based on those searches over aperiod of time for storage in search DB 304.

In step 502, server 306 receives search metric data from search DB 304.

In some embodiments, the search metric data include at least a pluralityof search strings, and interaction data corresponding to each of theplurality of search strings. Search strings, as used herein, refer tocharacter strings defining the subject matter of search operations. Forexample, search string may include terms such names of products whosedata may be stored in databases, thus the data of a product may belocated based on its name. Other examples of terms that may be part of asearch string include brand, nickname, attributes, serial numbers, tags,keywords, or other identifiers or properties associated with data of theproducts stored as entries in one or more databases (e.g. search DB304).

In some embodiments, the plurality of search strings are text stringsprovided from user devices. For example, the characters in the searchstrings may be text characters, and may correspond to examples of termsabove. User devices may refer to devices, such as PC computers, mobilephones, laptops, tablets, or other similar computing and communicationdevices associated with users. Users, as used herein, refer toindividuals who use the systems described in the disclosed embodiments,but who are not part of on-line retail platform. Examples of users mayinclude vendors, sellers, shoppers, browsers, or any other individualthat may perform search operations using the systems. By way examples,various searchers using devices 302 may provide search strings as theysearch for data stored in system 100.

Interaction data, as used herein, may refer to metric data that describeany interaction between the searchers and the systems and databases. Forexample, interaction data may include data such as, what strings areused, how often are searches performed, and interactions between thesearchers and the results generated. In some embodiments, theinteraction data may include a number of user interaction with searchresults of each of the plurality of search strings. For example, userinteractions with search result may provide an indication how searchersrespond to the result provided in response to queries of search strings,and may be useful for the systems to identify the most relevant results.Examples of user interaction may include accessing, viewing, tagging,saving, or bookmarking one or more of the results. User interaction mayalso subsequent actions taken by the searcher, such as purchasing of theproduct based on the search. In some embodiments, the interaction datamay include instances of user selection. User selection may refer tointeraction between a searcher and the system that provide searchresults based on the search string provided by the searcher. From theresults provided, the searcher may select one or more of the results forviewing, accessing, saving, bookmarking, or some other action thesearcher may desire. For example, the searcher may view the results ingenerated user interfaces on the user device. The searcher may make auser selection by clicking, pressing, or otherwise select one or more ofthe results. The selection, or data generated by the selection, may betransmitted from the user device to the system. By way of examples,various searchers using devices 302 may view results of their search ondevices 302. These various searchers may make selections from theresults provided. These selections may be examples of user selection.

In some embodiments, the system may compile interaction data includinguser selection for a period of time. For example, each time a searcherperforms a search using search strings, the system will keep record ofthe search stings as metric data, the results generated from the searchstrings, and the user selection of the results. Thus, over a period oftime, the search metric data will contain among other data, whatsearchers searched for, what search strings are used, for each searchstring, what results were obtained and sent to the searchers and whichof the results were selected.

In step 504, server 306 receives first attributes from CDS 308.

In some embodiments, the systems may retrieve, from the one or moredatabases, a plurality of first product identifiers associated with theinteraction data. A product identifier may refer to data that uniquelyidentifies an entry stored in a database corresponding to a product. Forexample, a product identifier may include serial number, tag, stockkeeping unit (SKU), name, code, and/or other identifying information.Various different data relating to the same product may be linked viathe product identifier when stored in the database. In some embodiments,there may be multiple databases, each containing entries associated witha plurality of product identifier. First product identifiers may relateto entries stored in a first database, second product identifier mayrelate to entries stored in a second database, and so on. By way ofexample, as depicted in FIG. 3, the first product identifiers may beproduct identifiers received from search DB 304 as part of theinteraction data, and second product identifier may be the productidentifier received from vendor device 314.

In step 506, server 306 matches interaction data to the first productidentifier.

In some embodiments, the first identifiers may be associated withinteraction the based on user selection. For example, when searchersperform searches to locate database entries in system 100 correspondingto products, the results may be a listing of product identifiers of thedatabase entries for the searcher to select. Thus, in an example of asearch, a searcher uses search string to search for an entry in adatabase. The results of the search may include several entries, each ofwhich has a corresponding product identifier. When the searcher selectsone of these entries, the corresponding product identifier is thusassociated with interaction data. When there are many searchersperforming searches over a period of time, there may be repetition ofsearch strings. For example, many different searchers may search for“Nike shoes white,” “Samsung TV 50″,” or other similar common searchterms shoppers may use for product search. While the results generatedfrom the same search strings would be the same, different searchers mayselect different results. For example, one searcher may search for “Nikeshoes white” and selects product 11 (listed in FIG. 4) from among theresults, while a second searcher may also search for “Nike shoes white”and selects product 435 (listed in FIG. 4). Thus, the system maygenerate a record of which search terms correspond to which product, anda frequency of a product being select from a search by a particularsearch term. This record may be stored as interaction data and be storedwith other search metric data in a database (e.g. search DB 304).

In some embodiments, the plurality of first product identifiers may eachhave one or more first attributes. Attributes may refer to data thatdescribes one or more properties of a product, such as its brand, use,dimension, weight, color, or any such data that may be relevant to someaspects of a product. For example, a laptop may include attributes suchas screen size, weight, battery life, memory, processing speed, etc. Inanother example, a TV may include attributes such as type of display(plasma/LED/LCD), resolution, output interface, power consumption, etc.In yet another example, a toy of interlocking plastic bricks may includeattributes such as number of pieces, material, suggested age of user,etc. In yet another example, a shoe may include attributes such asbrand, color, style, etc. A person of ordinary skill in the art willappreciate that other examples of products belonging to differentcategories may include other type attributes. By of way of example,server 306 receives attributes associated with the first productidentifiers from CDS DB 308.

In step 508, server 306 generates an interaction table.

In some embodiments, the systems may generate, based on the searchmetric data and the plurality of first product identifiers, a table, thetable including the plurality of search strings ranked by thecorresponding interaction data. As described previously, interactiondata may include a frequency of a product being select from a search bya particular search string. A table may thus be generated containing alist of commonly used search strings. For each of the commonly searchstrings, the table may also include the number of times the searchstring is used and the commonly selected products associated and thefrequencies of their selection. This list of commonly used searchstrings may be ranked based on the number of times the search stringsare used.

In some embodiments, generating the table comprises formatting theplurality of search strings. Example of formatting may include copy,transfer, retrieve, format, truncate, sort, and/or otherwise manipulatesearch strings into appropriate format suitable for the systems in use.In some embodiments, generating the table includes removing undesiredsearch strings from the plurality of search strings. Some search stringsprovided by searchers may be unneeded or inappropriate. For example,certain search strings may be unintelligible or may be provided in alanguage not supported by the systems (e.g. system 100), and thus thesestrings provide little value to the systems, and may be excluded fromthe table. In another example, certain search strings may contain vulgaror offensive language, and should be removed. A list of vulgar/offensivestrings may be stored in one or more databases (e.g. CDS DB 308, oranother separate database connected to system 100 not depicted), andsearch strings containing terms on the list may be removed.

In some embodiments, generating the table includes associating, based onthe search metric data, each interaction of the plurality of searchstrings with one or more first product identifiers. In some embodiments,the interaction may be user selection as described previously. Forexample, each instance that searcher selects from the list of resultsbased on a particular search string, the system associates the firstproduct identifier of the selected result with that particular searchstring. The system may maintain counters to record a frequency of eachproduct identifier (e.g., number times a product identifier is selectedfor that particular search string). By way of example depicted in FIG.4, searchers selected product 11 for “Nike shoes white” 865 times;selected product 435 for “Nike shoes white” 34 times; selected product11 for “Sport shoes” 76 times; and selected product 34 for “Samsung TV50″” 652 times.

In some embodiments, generating the interaction table includes ranking,for each of the plurality of search strings, one or more first productidentifiers. The product identifiers may be ranked, for example, bytheir respective frequency.

In step 510, server 306 generates lists from the interaction table.

In some embodiments, the systems may generate one or more relevantlists, the relevant lists comprise the plurality of search stringshaving corresponding interaction data above one or more thresholdvalues. An example of a threshold value may be the total number of userselection for each of the plurality of search strings.

In some embodiments, the one or more relevant lists include a firstrelevant list and a second relevant list. In some embodiments, the firstrelevant list includes the plurality of search strings havingcorresponding interaction data above a first threshold value, and thesecond relevant list includes the plurality of search strings havingcorresponding interaction data above a second threshold value. In someembodiments, the first threshold value is greater than the secondthreshold value. In some embodiments, the first threshold and the secondthreshold may be a ranking of the search string in term of searchfrequency. For example, the top ranked search string is the topmostsearched, the 1000^(th) ranked search string is the 10000^(th) mostsearched, and so on. In some embodiments, the first relevant listcontains search strings that are top-query search strings (rankingbetween 1^(st) and 9999^(th)), and the second relevant list containtorso-query search strings (ranking between 10000^(th) ranked and79999^(th)). In some embodiments, there may be a third relevant list,and may contain tail-query search strings (ranking between 80000^(th)and 180000^(th)).

In some embodiments, generating the one or more relevant lists includes,populating a first relevant list with the first product identifiershaving a ranking above a first threshold value. For example, from thetable generated containing the plurality of search strings and theirrespective interaction data, each product identifiers may be ranked baseon its frequency, from the most to the least. The first threshold may bea predetermined value of frequency. The second threshold value may beanother predetermined value of frequency that is less than the firstthreshold value. Thus, the first relevant list will contain the firstproduct identifiers having frequencies greater than or equal to thefirst threshold value. In some embodiments, generating the one or morerelevant lists include populating a second relevant list with the firstproduct identifiers having ranking above a second threshold value andbelow the first threshold value. The second relevant list will containthe first product identifiers having frequencies less than the firstthreshold value and greater than or equal to the second thresholdvalues.

As described previously, each of the first product identifiers in thetable is associated with one or more search strings, based on which ofthe first product identifiers are selected. Thus, the first relevantlist and the second relevant list contain, for each of the first productidentifiers in the respectively list, the associated search strings. Forexample, as depicted in FIG. 4, table 403 may be an example of the firstor the second relevant list. Table 403 may include product 11 andproduct 34 because the corresponding frequencies for their searchstrings are either top-query search strings or torso-query searchstings. Product 435 from table 401 may be excluded from table 403because the because the corresponding search strings are neithertop-query search strings nor torso query search strings.

In step 512, server 306 stores the lists in inverted format.

In some embodiments, generating the one or more relevant lists includestoring the first relevant list and the second relevant list in aninverted index format. Inverted index format may refer to methods oralgorithms for indexing files and data for storage in databases thatallow for quick and efficient retrieval by an inverted index searchengine. An inverted index/search may refer to methods or algorithms forsearching and indexing data in databases where individual terms areindexed and directed to its location/or entry, in contrast to a forwardindex/search where locations/entries of the terms are indexed anddirected to the terms.

FIG. 6 is a flow chart depicting an exemplary process for generatingkeywords for searches consistent with the disclosed embodiments.

In step 602, server 306 receives new product registration.

In some embodiments, step 602 comprises retrieving data relating to asecond product identifier. Second product identifiers may refer toproduct identifiers that server 306 retrieves from databases or sourcedifferent from that of the first product identifiers. By way of example,as illustrated in FIG. 3, server 306 receives the second productidentifier from vendor device 314. In some instances, as part ofregistering new product, server 306 may also receive data relating tothe second identifier from vendor device 314. In some embodiments, theserver 306 may extract, from the data relating to a second productidentifier, one or more second attribute data of the second productidentifier. The second attributes may be information relating toproperties associated with a product identified by the second productidentifier, similar to the first attributes. In some embodiments, thesecond attributes may be product name keywords, tags, or other textstrings associated with the second product identifier. In someembodiments, the second product may lack product information, and thesecond contributes may consist of only the product name. By way ofexample, server 306 may receive the second attributes along with thesecond identifier from vendor device 314.

In some embodiments, the first product identifiers represent existingproducts that have already been processed by the system, and alreadydeemed to possess keywords, tags, attributes, and other similar textsearchable strings that enable them to be readily locatable in adatabase. The second product identifier may represent a new product thatis being registered into the systems' (e.g. system 100) database.

In step 604, server 306 searches lists for matching attributes.

In some embodiments, server 306 may perform searches in the relevantlists using the one or more second attribute data. In some embodiments,performing searches includes generating one or more search terms basedon the second attribute data of the second product identifier. Forexample, server 306 may generate a search term based on the product nameof the second product identifier.

In some embodiments, performing searches includes performing invertedindex searches based on the search terms for matches in the firstattributes of the first product identifiers stored the first relevantlist and the second relevant list. As discussed above, each of the firstproduct identifiers are associated with one or more first attributes.Systems may perform inverted index search through the first relevantlist and/or the second relevant list for first attributes of firstproduct identifiers on those lists. In some embodiments, when the searchterms consist of a product name of the second product identifier, server306 performs inverted index searches for the product name of the firstproduct identifiers on the first and/or second relevant lists.

In step 606, server 306 determine match ranking.

In some embodiments, step 606 includes assigning, based on apredetermined rule, keywords to the data relating to the secondidentifier, the keywords being one or more of the plurality of searchstrings. A predetermined rule may be an algorithm or logic, executed byserver 306, for determining whether assigning the keywords isappropriate. Once server 306 determine that assigning the keywords isappropriate, server 306 will assign one or more of the plurality ofsearch strings as keywords to the second identifier.

In some embodiments, the predetermined rule may include determining, foreach of the identified first product identifiers, a rank of matching,the rank of matching being based on a number of the searching terms thatmatches the first attributes. A higher number of matches may indicate ahigher ranking. For example, the first product identifiers that haveproduct names that are more similar to the product name of the secondproduct identifier are ranked higher.

FIG. 7 depicts an illustrated exemplary process for determining aranking of matching, consistent with the disclosed embodiments. Forexamples, in step 602, a vendor registers a new shoe product having theproduct name of “Nike Air Max fly sneaker M AT2506-100.” In someembodiments, search terms may be formed from a combination of terms fromthe product name. In the example depicted in FIG. 7, one of the first orsecond lists that may contain first product identifiers having names of“Nike AIR MAX 97 triple white sneakers,” “Nike Phantom Football,” “NikeCourt Royal SL sneakers,” and “Nike Air Max 270 White University.” Instep 604, server 306 performs an inverted index search for first productidentifiers having product names matching the product names of thesecond product identifier. As depicted in the example of FIG. 7, theresult of the search is ranked based a likelihood of match of theproduct names. Column 702 is the rank of matching, rank 1 being the mostlikely matched. Column 704 represents the first product identifiers.Column 706 includes the keywords corresponding to each of the firstproduct identifier firers in column 704. Column 708 includes thefrequency corresponding to the keywords in column 706.

Referring back to FIG. 6, in step 608, server 306 determine whether athreshold value is exceeded.

In some embodiments, the predetermined rule may include determining arelevancy status for each of the identified first product identifiersbased on the ranking of matching, the frequency of a correspondingkeyword, a threshold ranking of matching and a threshold frequency. Therelevancy status may be a decision by server 306 on whether to assign akey term to the second product identifier. Recall from previousdiscussion that the keywords may be search strings provided by devices302. As part of the interaction data, every search string may be rankedby the frequency of use into categories of top-query, torso-query,and/or tail-query. Search strings are these categories are separatedinto the first relevant list, the second relevant list, and the thirdrelevant list. A person of ordinary skill in the art will appreciatethat the particular specific ranking position of the first rank and thesecond rank may be adjusted as needed.

In some embodiments, the thresholds for assigning keywords in the firstrelevant list may be a maximum rank of 4 and a minimum frequency of 13.Maximum rank refers to the ranking with respect to column 702, andminimum frequency refers to numbers click with respect to column 704.For keywords in the second relevant list, the thresholds may be amaximum rank of 1 and a minimum frequency of 4. For keywords in thethird relevant list, the thresholds may be a maximum rank of 3 andminimum frequency of 60. A person of ordinary skill in the art willunderstand that the rank and frequency number may be adjusted as neededas to include the desired products in the appropriate list.

For example, if the keyword “Nike football” is included in the firstrelevant list, then server 306 will assign it to the new product, sinceits rank in column 702 is 2 (less than maximum rank of 4), and it has139 clicks (greater than 4). If, however, “Nike football” is included inthe second relevant list, then server 306 will not assign it to the newproduct, since its rank column is 2 (greater than maximum rank of 1).

Referring back to FIG. 6, if determination is ‘No’ in step 608, in step612, server 306 does not assign keywords to the new product registeredin step 612. If determination is ‘Yes’ in step 608, in step 610, server306 assign keywords to the new product registered in step 602. Forexample, in the example depicted, if server 306 determines “Yes” in step608 for the keyword “Nike football”, the new product will be assigned“Nike football”, so that any this new product may be found duringsearches or be promoted by the on-line retail system when “Nikefootball” is searched by a user.

While the present disclosure has been shown and described with referenceto particular embodiments thereof, it will be understood that thepresent disclosure can be practiced, without modification, in otherenvironments. The foregoing description has been presented for purposesof illustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, or other opticaldrive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. Various programs orprogram modules can be created using any of the techniques known to oneskilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A method of generating keywords for searches,comprising: retrieving, from one or more database, search metric datafor a predetermined time period, the search metric comprise at least aplurality of search strings, and interaction data corresponding to eachof the plurality of search strings; retrieving, from the one or moredatabase, a plurality of first product identifiers associated with theinteraction data, the plurality of first product identifiers each havingone or more first attributes; generating, based on the search metricdata and the plurality of first product identifiers, a table, the tablecomprising the plurality of search strings ranked by the correspondinginteraction data; generating one or more relevant lists, the relevantlists comprise the plurality of search strings having correspondinginteraction data above one or more threshold values; retrieving datarelating to a second product identifier; extracting, from the data, oneor more second attributes of the second product identifier; performingsearches in the relevant lists using the one or more second attributedata; and assigning, based on a predetermined rule, keywords to the datarelating to the second identifier, the keywords being one or more of theplurality of search strings.
 2. The method of claim 1, wherein aplurality of search strings are text strings provided from user devices.3. The method of claim 1, wherein the relevant lists comprises a firstrelevant list and a second relevant list, wherein the first relevantlist comprise the plurality of search strings having correspondinginteraction data above a first threshold value, and wherein the secondrelevant list comprise the plurality of search strings havingcorresponding interaction data above a second threshold value, the firstthreshold value being greater than the second threshold value.
 4. Themethod of claim 1, wherein generating the table comprises: formattingthe plurality of search strings; removing undesired search strings fromthe plurality of search strings; associate, based on the search metricdata, each interaction of the plurality of search strings with one ormore first product identifiers; and ranking, for each of the pluralityof search strings, one or more first product identifiers.
 5. The methodof claim 4, wherein generating relevant lists comprises: populating afirst relevant list with the first product identifiers having rankingabove a first threshold value; populating a second relevant list withthe first product identifiers having ranking above a second thresholdvalue and below the first threshold value; and storing the firstrelevant list and the second relevant list in an inverted index format.6. The method of claim 5, performing searches in the relevant listsusing the one or more attribute data comprise: generating one or moresearch terms based on the second attributes of the second productidentifier; performing inverted index searches based on the search termsfor matches in the first attributes of the first product identifiersstored the first relevant list and the second relevant list; andidentifying the first product identifiers associated with the results ofthe inverted index searches.
 7. The method of claim 6, wherein assigningthe keywords comprises: determining, for each of the identified firstproduct identifiers, a probability of matching, the probability ofmatching being based on a number of the searching terms that matches thefirst attributes; determining, for each of the first identified productidentifiers, based on the associated interaction data, a frequency; anddetermining a relevancy status for each of the identified first productidentifiers based on the probability of matching, the frequency, athreshold probability of matching and a threshold frequency; andupdating the data of the second product identifier to include the searchstrings associated the first identified product identifiers based on therelevancy status.
 8. The method of claim 7, wherein the first identifiedproduct identifiers contained in the first relevant list have differentvalues of the threshold probability of matching and the thresholdfrequency than the first identified product identifiers contained in thesecond relevant list.
 9. The method of claim 1, wherein the interactiondata comprises a number of user interaction with search results of eachof the plurality of search strings.
 10. The method of claim 1, whereinthe interaction data comprises instances of user selection.
 11. A systemfor generating keywords for searches, comprising: one or more database;one or more storage media storing computer readable instruction; and atleast one processor configured to execute the stored instructions toperform the steps of: retrieving, from one or more database, searchmetric data for a predetermined time period, the search metric compriseat least a plurality of search strings, and interaction datacorresponding to each of the plurality of search strings; retrieving,from the one or more database, a plurality of first product identifiersassociated with the interaction data, the plurality of first productidentifiers each having one or more first attributes; generating, basedon the search metric data and the plurality of first productidentifiers, a table, the table comprising the plurality of searchstrings ranked by the corresponding interaction data; generating one ormore relevant lists, the relevant lists comprise the plurality of searchstrings having corresponding interaction data above one or morethreshold values; retrieving data relating to a second productidentifier; extracting, from the data, one or more second attributes ofthe second product identifier; performing searches in the relevant listsusing the one or more second attribute data; and assigning, based on apredetermined rule, keywords to the data relating to the secondidentifier, the keywords being one or more of the plurality of searchstrings.
 12. The system of claim 11, wherein a plurality of searchstrings are text strings provided from user devices.
 13. The system ofclaim 11, wherein the relevant lists comprises a first relevant list anda second relevant list, wherein the first relevant list comprise theplurality of search strings having corresponding interaction data abovea first threshold value, and wherein the second relevant list comprisethe plurality of search strings having corresponding interaction dataabove a second threshold value, the first threshold value being greaterthan the second threshold value.
 14. The system of claim 11, whereingenerating the table comprises: formatting the plurality of searchstrings; removing undesired search strings from the plurality of searchstrings; associate, based on the search metric data, each interaction ofthe plurality of search strings with one or more first productidentifiers; and ranking, for each of the plurality of search strings,one or more first product identifiers.
 15. The system of claim 14,wherein generating relevant lists comprises: populating a first relevantlist with the first product identifiers having ranking above a firstthreshold value; populating a second relevant list with the firstproduct identifiers having ranking above a second threshold value andbelow the first threshold value; and storing the first relevant list andthe second relevant list in an inverted index format.
 16. The system ofclaim 15, performing searches in the relevant lists using the one ormore attribute data comprise: generating one or more search terms basedon the second attributes of the second product identifier; performinginverted index searches based on the search terms for matches in thefirst attributes of the first product identifiers stored the firstrelevant list and the second relevant list; and identifying the firstproduct identifiers associated with the results of the inverted indexsearches.
 17. The system of claim 16, wherein assigning the keywordscomprises: determining, for each of the identified first productidentifiers, a probability of matching, the probability of matchingbeing based on a number of the searching terms that matches the firstattributes; determining, for each of the first identified productidentifiers, based on the associated interaction data, a frequency; anddetermining a relevancy status for each of the identified first productidentifiers based on the probability of matching, the frequency, athreshold probability of matching and a threshold frequency; andupdating the data of the second product identifier to include the searchstrings associated the first identified product identifiers based on therelevancy status.
 18. The system of claim 17, wherein the firstidentified product identifiers contained in the first relevant list havedifferent values of the threshold probability of matching and thethreshold frequency than the first identified product identifierscontained in the second relevant list.
 19. The system of claim 11,wherein the interaction data comprises a number of user interaction withsearch results of each of the plurality of search strings.
 20. A methodof generating keywords for searches, comprising: retrieving, from one ormore database, search metric data for a predetermined time period, thesearch metric comprise at least a plurality of search strings, andinteraction data corresponding to each of the plurality of searchstrings; retrieving, from the one or more database, a plurality of firstproduct identifiers associated with the interaction data, the pluralityof first product identifiers each having one or more first attributes;generating, based on the search metric data and the plurality of firstproduct identifiers, a table, the table comprising the plurality ofsearch strings ranked by the corresponding interaction data; generatingone or more relevant lists, the relevant lists comprise the plurality ofsearch strings having corresponding interaction data above one or morethreshold values; receiving, from one or more user device, productinformation of a second product identifier comprising at least a productname; extracting, based on the product name, one or more secondattributes data of the second product identifier; performing searches inthe relevant lists using the one or more attribute data; and assigning,based on a predetermined rule, keywords to the data relating to thesecond identifier, the keywords being one or more of the plurality ofsearch strings.